- Email: [email protected]
Enabling discovery science in human connectomics
Sci. Bull. DOI 10.1007/s11434-014-0716-5
www.scibull.com www.springer.com/scp
Research Highlight
Enabling discovery science in human connectomics Olaf Sporns
Received: 15 December 2014 / Accepted: 18 December 2014 Ó Science China Press and Springer-Verlag Berlin Heidelberg 2014
In recent years, the confluence of technological advances in human neuroimaging and quantitative tools for understanding the structure and dynamics of complex systems have led to the emergence of new approaches toward understanding human brain function. These new approaches conceptualize the brain as a network organized across different spatial and temporal scales—a distributed complex system whose integrated function underlies human behavior and cognition. A core objective for this new conceptual approach is the construction of accurate and comprehensive network maps of human brain structural connectivity (the human connectome [1]) and its corresponding temporally fluctuating patterns of functional connectivity. In this issue of Science Bulletin, Xu et al. [2] describe a computational pipeline, the Connectome Computation System (CCS), designed to enable researchers to preprocess, data mine and explore the complex patterns of human brain connectivity. The science of mapping and modeling the networks of the human brain has made considerable progress in recent years. Connectome studies have demonstrated a number of characteristic aspects of network topology, for example, a strong tendency toward high local clustering, short global path length and the existence of structural modules interconnected by a small subset of highly linked hub regions [3]. For the most part, these structural attributes are shared with network features found in the brains of ‘‘model organisms,’’ from invertebrate species all the way to nonhuman primates. Importantly, individual variations in brain
O. Sporns (&) Department of Psychological and Brain Sciences and Indiana University Network Science Institute, Indiana University, Bloomington, IN 47405, USA e-mail: [email protected]
connectivity have been shown to exhibit characteristic developmental trajectories across the human lifespan [4] and to correlate with aspects of individual variations in human behavioral and cognitive performance [5]. These intriguing findings suggest that the systematic exploration of patterns of brain connectivity may shed new light on the biological bases of individual differences in psychological and cognitive function. The systematic study of brain connectivity at the level of individual subjects faces a number of methodological challenges, several of which are outlined in the article by Xu et al. [2]. One set of challenges arises from the need to collect and share ‘‘big data’’ on brain structure and function from large samples of individuals, e.g., those comprising the 1,000 Functional Connectomes Project [6] and the Human Connectome Project [7]. A related set of challenges result from the requirement to develop processing tools that are flexible, reliable and extendable to new innovations in data analysis or new measures of brain network organization. Creating such processing tools that are both methodologically robust and rigorous as well as easily reconfigurable to adapt to future scientific problems is absolutely essential for enabling the ultimate success of individual connectomics. It is exactly this set of challenges that the approach outlined in Xu et al. [2] aims to address. The core of the article is a description of the CCS, a computational processing and analysis pipeline for largescale multimodal human neuroimaging data. CCS has several distinctive features. It is explicitly conceived along the lines of a hierarchy with three somewhat separate levels, respectively, dedicated to data cleaning and pre-processing, the construction of connectome maps from individual brain data and subsequent data mining and discovery. Embedded in this three-step hierarchy are numerous ‘‘functional modules’’ that encapsulate specific statistical tools and algorithms dedicated
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Sci. Bull.
Fig. 1 Individual differences in functional connectivity (FC) across the human lifespan. The plots show lifespan changes in FC averages within (left) and between (right) resting-state networks (RSN). FC data were computed using CCS from 126 individual subjects. Reproduced with permission from reference [10]
to various sub-tasks. The modular design allows reconfiguring the pipeline to match specific scientific goals and requirements. Xu et al. survey the design of CCS along the different hierarchical levels. At the first level of data preprocessing, different modules perform essential functions that are specific to structural, diffusion and resting-state fMRI data, respectively, capped by a module dedicated to quality control. At the second level of the hierarchy, individual connectomes (structural and functional) are constructed from measurements of brain morphology, connectional anatomy and functional connectivity. Here, CCS incorporates various utilities for computing structural graphs from measures of white matter architecture, as well as sparsified correlation matrices from fMRI time series. Importantly, CCS aims to remain open to future developments in this area, for example, new algorithms for extracting or estimating the weights of structural and functional edges. Finally, the third hierarchical level takes individual connectomes and provides tools for enabling ‘‘human brain discovery science’’—the exploration of the connectivity structure with analysis approaches ranging from statistical methods such as regression to machine learning and visualization. As part of this hierarchical level, CCS offers sophisticated tools for estimating test–retest reliability and reproducibility of individual connectomes [8]. This addresses an extremely important problem that relates to issues such as the establishment of uniform data analysis standards across studies and that must be successfully addressed in order to enable the reliable identification of network features that predict aspects of behavior and cognition. Beyond describing the capabilities of CCS, Xu et al. make a strong case for promoting ‘‘open science’’ through the sharing of both data and analysis pipelines. Earlier efforts (e.g., [6]) have set examples for how data sharing can promote discovery science across the discipline and lead to the adoption of more uniform acquisition and analysis approaches. More recently, Zuo et al. [9] have expanded on these earlier efforts by releasing over 5,000 data sets from 1,629 individuals through the international
123
Consortium for Reliability and Reproducibility. Another important public data set comprises structural and functional data from a large cohort of individuals sampled across the human lifespan, with ages ranging from 6 to 85 years. Processed with earlier versions of CCS, these data have yielded important new insights into developmental patterns in the organization and interconnectivity of resting-state networks [4] and their relation to underlying structural networks of the human brain (Fig. 1) [10]. The article by Xu et al. further demonstrates the capabilities of CCS by showing intriguing preliminary data on the developmental trajectories of cortical thickness and surface area for specific resting-state networks. These data suggest that individual resting-state networks follow different time courses in their lifespan development. Creating reliable, reconfigurable and extensible data processing pipelines is essential for the future growth of human connectomics. Xu et al. make an important contribution by developing CCS along the lines of a hierarchical modular design that enables the incorporation of future capabilities and promotes high standards of reproducibility. While much more work surely remains to be done it seems certain that a methodological framework based on knowledge discovery and open science such as the one supported by Xu et al. will be essential for catalyzing future progress in this area.
References 1. Sporns O, Tononi G, Ko¨tter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42 2. Xu T, Yang Z, Jiang L et al (2015) A connectome computation system for discovery science of brain. Sci Bull 60:86–95 3. Sporns O (2014) Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci 17:652–660 4. Cao M, Wang JH, Dai ZJ et al (2014) Topological organization of the human brain functional connectome across the lifespan. Develop Cogn Neurosci 7:76–93 5. Li Y, Liu Y, Li J et al (2009) Brain anatomical network and intelligence. PLoS Comput Biol 5:e1000395 6. Biswal BB, Mennes M, Zuo XN et al (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107:4734–4739 7. Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79 8. Zuo XN, Xing XX (2014) Test–retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neurosci Biobehav Rev 45:100–118 9. Zuo XN, Anderson JS, Bellec P et al (2014) An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data 1:140049 10. Betzel RF, Byrge L, He Y et al (2014) Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 102:345–357
www.scibull.com www.springer.com/scp
Research Highlight
Enabling discovery science in human connectomics Olaf Sporns
Received: 15 December 2014 / Accepted: 18 December 2014 Ó Science China Press and Springer-Verlag Berlin Heidelberg 2014
In recent years, the confluence of technological advances in human neuroimaging and quantitative tools for understanding the structure and dynamics of complex systems have led to the emergence of new approaches toward understanding human brain function. These new approaches conceptualize the brain as a network organized across different spatial and temporal scales—a distributed complex system whose integrated function underlies human behavior and cognition. A core objective for this new conceptual approach is the construction of accurate and comprehensive network maps of human brain structural connectivity (the human connectome [1]) and its corresponding temporally fluctuating patterns of functional connectivity. In this issue of Science Bulletin, Xu et al. [2] describe a computational pipeline, the Connectome Computation System (CCS), designed to enable researchers to preprocess, data mine and explore the complex patterns of human brain connectivity. The science of mapping and modeling the networks of the human brain has made considerable progress in recent years. Connectome studies have demonstrated a number of characteristic aspects of network topology, for example, a strong tendency toward high local clustering, short global path length and the existence of structural modules interconnected by a small subset of highly linked hub regions [3]. For the most part, these structural attributes are shared with network features found in the brains of ‘‘model organisms,’’ from invertebrate species all the way to nonhuman primates. Importantly, individual variations in brain
O. Sporns (&) Department of Psychological and Brain Sciences and Indiana University Network Science Institute, Indiana University, Bloomington, IN 47405, USA e-mail: [email protected]
connectivity have been shown to exhibit characteristic developmental trajectories across the human lifespan [4] and to correlate with aspects of individual variations in human behavioral and cognitive performance [5]. These intriguing findings suggest that the systematic exploration of patterns of brain connectivity may shed new light on the biological bases of individual differences in psychological and cognitive function. The systematic study of brain connectivity at the level of individual subjects faces a number of methodological challenges, several of which are outlined in the article by Xu et al. [2]. One set of challenges arises from the need to collect and share ‘‘big data’’ on brain structure and function from large samples of individuals, e.g., those comprising the 1,000 Functional Connectomes Project [6] and the Human Connectome Project [7]. A related set of challenges result from the requirement to develop processing tools that are flexible, reliable and extendable to new innovations in data analysis or new measures of brain network organization. Creating such processing tools that are both methodologically robust and rigorous as well as easily reconfigurable to adapt to future scientific problems is absolutely essential for enabling the ultimate success of individual connectomics. It is exactly this set of challenges that the approach outlined in Xu et al. [2] aims to address. The core of the article is a description of the CCS, a computational processing and analysis pipeline for largescale multimodal human neuroimaging data. CCS has several distinctive features. It is explicitly conceived along the lines of a hierarchy with three somewhat separate levels, respectively, dedicated to data cleaning and pre-processing, the construction of connectome maps from individual brain data and subsequent data mining and discovery. Embedded in this three-step hierarchy are numerous ‘‘functional modules’’ that encapsulate specific statistical tools and algorithms dedicated
123
Sci. Bull.
Fig. 1 Individual differences in functional connectivity (FC) across the human lifespan. The plots show lifespan changes in FC averages within (left) and between (right) resting-state networks (RSN). FC data were computed using CCS from 126 individual subjects. Reproduced with permission from reference [10]
to various sub-tasks. The modular design allows reconfiguring the pipeline to match specific scientific goals and requirements. Xu et al. survey the design of CCS along the different hierarchical levels. At the first level of data preprocessing, different modules perform essential functions that are specific to structural, diffusion and resting-state fMRI data, respectively, capped by a module dedicated to quality control. At the second level of the hierarchy, individual connectomes (structural and functional) are constructed from measurements of brain morphology, connectional anatomy and functional connectivity. Here, CCS incorporates various utilities for computing structural graphs from measures of white matter architecture, as well as sparsified correlation matrices from fMRI time series. Importantly, CCS aims to remain open to future developments in this area, for example, new algorithms for extracting or estimating the weights of structural and functional edges. Finally, the third hierarchical level takes individual connectomes and provides tools for enabling ‘‘human brain discovery science’’—the exploration of the connectivity structure with analysis approaches ranging from statistical methods such as regression to machine learning and visualization. As part of this hierarchical level, CCS offers sophisticated tools for estimating test–retest reliability and reproducibility of individual connectomes [8]. This addresses an extremely important problem that relates to issues such as the establishment of uniform data analysis standards across studies and that must be successfully addressed in order to enable the reliable identification of network features that predict aspects of behavior and cognition. Beyond describing the capabilities of CCS, Xu et al. make a strong case for promoting ‘‘open science’’ through the sharing of both data and analysis pipelines. Earlier efforts (e.g., [6]) have set examples for how data sharing can promote discovery science across the discipline and lead to the adoption of more uniform acquisition and analysis approaches. More recently, Zuo et al. [9] have expanded on these earlier efforts by releasing over 5,000 data sets from 1,629 individuals through the international
123
Consortium for Reliability and Reproducibility. Another important public data set comprises structural and functional data from a large cohort of individuals sampled across the human lifespan, with ages ranging from 6 to 85 years. Processed with earlier versions of CCS, these data have yielded important new insights into developmental patterns in the organization and interconnectivity of resting-state networks [4] and their relation to underlying structural networks of the human brain (Fig. 1) [10]. The article by Xu et al. further demonstrates the capabilities of CCS by showing intriguing preliminary data on the developmental trajectories of cortical thickness and surface area for specific resting-state networks. These data suggest that individual resting-state networks follow different time courses in their lifespan development. Creating reliable, reconfigurable and extensible data processing pipelines is essential for the future growth of human connectomics. Xu et al. make an important contribution by developing CCS along the lines of a hierarchical modular design that enables the incorporation of future capabilities and promotes high standards of reproducibility. While much more work surely remains to be done it seems certain that a methodological framework based on knowledge discovery and open science such as the one supported by Xu et al. will be essential for catalyzing future progress in this area.
References 1. Sporns O, Tononi G, Ko¨tter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42 2. Xu T, Yang Z, Jiang L et al (2015) A connectome computation system for discovery science of brain. Sci Bull 60:86–95 3. Sporns O (2014) Contributions and challenges for network models in cognitive neuroscience. Nat Neurosci 17:652–660 4. Cao M, Wang JH, Dai ZJ et al (2014) Topological organization of the human brain functional connectome across the lifespan. Develop Cogn Neurosci 7:76–93 5. Li Y, Liu Y, Li J et al (2009) Brain anatomical network and intelligence. PLoS Comput Biol 5:e1000395 6. Biswal BB, Mennes M, Zuo XN et al (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107:4734–4739 7. Van Essen DC, Smith SM, Barch DM et al (2013) The WU-Minn human connectome project: an overview. Neuroimage 80:62–79 8. Zuo XN, Xing XX (2014) Test–retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: a systems neuroscience perspective. Neurosci Biobehav Rev 45:100–118 9. Zuo XN, Anderson JS, Bellec P et al (2014) An open science resource for establishing reliability and reproducibility in functional connectomics. Sci Data 1:140049 10. Betzel RF, Byrge L, He Y et al (2014) Changes in structural and functional connectivity among resting-state networks across the human lifespan. Neuroimage 102:345–357